Relevant bibliographies by topics / Linear classification methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Linear classification methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Linear classification methods"

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Zhou, Jianhang, Shaoning Zeng, and Bob Zhang. "Linear Representation-Based Methods for Image Classification: A Survey." IEEE Access 8 (2020): 216645–70. http://dx.doi.org/10.1109/access.2020.3041154.

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Nikolov, Hristo S., Doyno I. Petkov, Nina Jeliazkova, Stela Ruseva, and Kiril Boyanov. "Non-linear methods in remotely sensed multispectral data classification." Advances in Space Research 43, no. 5 (2009): 859–68. http://dx.doi.org/10.1016/j.asr.2008.06.009.

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Byrnes, Christopher I., and Peter E. Crouch. "Geometric methods for the classification of linear feedback systems." Systems & Control Letters 6, no. 4 (1985): 239–46. http://dx.doi.org/10.1016/0167-6911(85)90074-x.

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Schmah, Tanya, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, and Stephen C. Strother. "Comparing Classification Methods for Longitudinal fMRI Studies." Neural Computation 22, no. 11 (2010): 2729–62. http://dx.doi.org/10.1162/neco_a_00024.

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We compare 10 methods of classifying fMRI volumes by applying them to data from a longitudinal study of stroke recovery: adaptive Fisher's linear and quadratic discriminant; gaussian naive Bayes; support vector machines with linear, quadratic, and radial basis function (RBF) kernels; logistic regression; two novel methods based on pairs of restricted Boltzmann machines (RBM); and K-nearest neighbors. All methods were tested on three binary classification tasks, and their out-of-sample classification accuracies are compared. The relative performance of the methods varies considerably across sub
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Taskin, A. S., E. M. Mirkes, and N. Y. Sirotinina. "Application of the Fuzzy Classification for Linear Hybrid Prediction Methods." Modeling and Analysis of Information Systems 20, no. 3 (2015): 108–20. http://dx.doi.org/10.18255/1818-1015-2013-3-108-120.

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Hadad, Yossi, and Baruch Keren. "ABC inventory classification via linear discriminant analysis and ranking methods." International Journal of Logistics Systems and Management 14, no. 4 (2013): 387. http://dx.doi.org/10.1504/ijlsm.2013.052744.

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Ştefan, Raluca-Mariana, Măriuţa Şerban, Iulian-Ion Hurloiu, and Bianca-Florentina Rusu. "Kernel Methods for Data Classification." International conference KNOWLEDGE-BASED ORGANIZATION 22, no. 3 (2016): 572–75. http://dx.doi.org/10.1515/kbo-2016-0098.

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Abstract In the past decades, the exponential evolution of data collection for macroeconomic databases in digital format caused a huge increase in their volume. As a consequence, the automatic organization and the classification of macroeconomic data show a significant practical value. Various techniques for categorizing data are used to classify numerous macroeconomic data according to the classes they belong to. Since the manual construction of some of the classifiers is difficult and time consuming, are preferred classifiers that learn from action examples, a process which forms the supervi
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HSIEH, PI-FUEI, MING-HUA YANG, YI-JAY GU, and YU-CHENG LIANG. "CLASSIFICATION-ORIENTED LOCALLY LINEAR EMBEDDING." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (2010): 737–62. http://dx.doi.org/10.1142/s0218001410008159.

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The locally linear embedding (LLE) algorithm is hypothetically able to find a lower dimensional space than a linear method for preserving a data manifold originally embedded in a high dimensional space. However, uneven sampling over the manifold in real-world data ultimately causes LLE to suffer from the disconnected-neighborhood problem. Consequently, the final dimensionality required for the data manifold is multiplied by the number of disjoint groups in the complete data representation. In addition, LLE as an unsupervised method is unable to suppress between-class connections. This means th
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Puengnim, Anchalee, Nathalie Thomas, Jean-Yves Tourneret, and Josep Vidal. "Classification of linear and non-linear modulations using the Baum–Welch algorithm and MCMC methods." Signal Processing 90, no. 12 (2010): 3242–55. http://dx.doi.org/10.1016/j.sigpro.2010.05.030.

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Mohanavalli, S., S. Karthika, Srividya ., K. R.Uthayan, and N. Sandya. "Categorisation of Tweets Using Ensemble Classification Methods." International Journal of Engineering & Technology 7, no. 3.12 (2018): 722. http://dx.doi.org/10.14419/ijet.v7i3.12.16463.

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Twitter is a micro-blogging site that facilitates users to exchange short messages. Twitter is predominantly used in fields like business, healthcare, education and nation security. Twitter is being used by a large number of users for updating real time information and sentiment expression. The objective of this paper is to automate the classification of tweets into particular category using various machine learning algorithms like naïve bayes, SVM, and linear regression model. The proposed ensemble model aims to improve performance metrics of these algorithms. A comparative study of the algor
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Dissertations / Theses on the topic "Linear classification methods"

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Shin, Dongcheol. "Evaluation of Methods for Improving Classifying Cyclone Performance." Thesis, Virginia Tech, 2007. http://hdl.handle.net/10919/42679.

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Most mineral and coal processing plants are forced to size their particulate streams in order to maximize the efficiency of their unit operations. Classifiers are generally considered to be more practical than screens for fine sizing, but the separation efficiency decreases dramatically for particles smaller than approximately 150 μm. In addition, classifiers commonly suffer from bypass, which occurs when a portion of the ultrafine particles (slimes) are misplaced by hydraulic carryover into the oversize product. The unwanted misplacement can have a large adverse impact on downstream sepa
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Ernst, Oliver G. "Minimal and orthogonal residual methods and their generalizations for solving linear operator equations." Doctoral thesis, [S.l. : s.n.], 2000. https://tubaf.qucosa.de/id/qucosa%3A22355.

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This thesis is concerned with the solution of linear operator equations by projection methods known as minimal residual (MR) and orthogonal residual (OR) methods. We begin with a rather abstract framework of approximation by orthogonal and oblique projection in Hilbert space. When these approximation schemes are applied to sequences of nested spaces, with a simple requirement relating trial and test spaces in case of the OR method, one can derive at this rather general level the basic relations which have been proved for many specific Krylov subspace methods for solving linear systems of equat
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Louche, Ugo. "From confusion noise to active learning : playing on label availability in linear classification problems." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4025/document.

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Les travaux présentés dans cette thèse relèvent de l'étude des méthodes de classification linéaires, c'est à dire l'étude de méthodes ayant pour but la catégorisation de données en différents groupes à partir d'un jeu d'exemples, préalablement étiquetés, disponible en amont et appelés ensemble d'apprentissage. En pratique, l'acquisition d'un tel ensemble d'apprentissage peut être difficile et/ou couteux, la catégorisation d'un exemple étant de fait plus ardu que l'obtention de dudit exemple. Cette disparité entre la disponibilité des données et notre capacité à constituer un ensemble d'apprent
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Baur, Ulrike, Christopher Beattie, Peter Benner, and Serkan Gugercin. "Interpolatory Projection Methods for Parameterized Model Reduction." Universitätsbibliothek Chemnitz, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-201000011.

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We provide a unifying projection-based framework for structure-preserving interpolatory model reduction of parameterized linear dynamical systems, i.e., systems having a structured dependence on parameters that we wish to retain in the reduced-order model. The parameter dependence may be linear or nonlinear and is retained in the reduced-order model. Moreover, we are able to give conditions under which the gradient and Hessian of the system response with respect to the system parameters is matched in the reduced-order model. We provide a systematic approach built on established interpolatory
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Zhong, Xiao. "A study of several statistical methods for classification with application to microbial source tracking." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0430104-155106/.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: classification; k-nearest-neighbor (k-n-n); neural networks; linear discriminant analysis (LDA); support vector machines; microbial source tracking (MST); quadratic discriminant analysis (QDA); logistic regression. Includes bibliographical references (p. 59-61).
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Jung, Michael, and Todor D. Todorov. "On the Convergence Factor in Multilevel Methods for Solving 3D Elasticity Problems." Universitätsbibliothek Chemnitz, 2006. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200601510.

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The constant gamma in the strengthened Cauchy-Bunyakowskii-Schwarz inequality is a basic tool for constructing of two-level and multilevel preconditioning matrices. Therefore many authors consider estimates or computations of this quantity. In this paper the bilinear form arising from 3D linear elasticity problems is considered on a polyhedron. The cosine of the abstract angle between multilevel finite element subspaces is computed by a spectral analysis of a general eigenvalue problem. Octasection and bisection approaches are used for refining the triangulations. Tetrahedron, pentahedron and
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Bylesjö, Max. "Latent variable based computational methods for applications in life sciences : Analysis and integration of omics data sets." Doctoral thesis, Umeå universitet, Kemi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1616.

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With the increasing availability of high-throughput systems for parallel monitoring of multiple variables, e.g. levels of large numbers of transcripts in functional genomics experiments, massive amounts of data are being collected even from single experiments. Extracting useful information from such systems is a non-trivial task that requires powerful computational methods to identify common trends and to help detect the underlying biological patterns. This thesis deals with the general computational problems of classifying and integrating high-dimensional empirical data using a latent variabl
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Montagner, Igor dos Santos. "W-operator learning using linear models for both gray-level and binary inputs." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-21082017-111455/.

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Image Processing techniques can be used to solve a broad range of problems, such as medical imaging, document processing and object segmentation. Image operators are usually built by combining basic image operators and tuning their parameters. This requires both experience in Image Processing and trial-and-error to get the best combination of parameters. An alternative approach to design image operators is to estimate them from pairs of training images containing examples of the expected input and their processed versions. By restricting the learned operators to those that are translation inva
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Tozlu, Ceren. "Classification et modélisation statistique intégrant des données cliniques et d’imagerie par résonance magnétique conventionnelle et avancée." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1043/document.

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L'accident vasculaire cérébral et la sclérose en plaques figurent parmi les maladies neurologiques les plus destructrices du système nerveux central. L'accident vasculaire cérébral est la deuxième cause de décès et la principale cause de handicap chez l'adulte dans le monde alors que la sclérose en plaques est la maladie neurologique non traumatique la plus fréquente chez l'adulte jeune. L'imagerie par résonance magnétique est un outil important pour distinguer le tissu cérébral sain du tissu pathologique à des fins de diagnostic, de suivi de la maladie, et de prise de décision pour un traitem
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Thess, M. "Parallel Multilevel Preconditioners for Problems of Thin Smooth Shells." Universitätsbibliothek Chemnitz, 1998. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-199801416.

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In the last years multilevel preconditioners like BPX became more and more popular for solving second-order elliptic finite element discretizations by iterative methods. P. Oswald has adapted these methods for discretizations of the fourth order biharmonic problem by rectangular conforming Bogner-Fox-Schmidt elements and nonconforming Adini elements and has derived optimal estimates for the condition numbers of the preconditioned linear systems. In this paper we generalize the results from Oswald to the construction of BPX and Multilevel Diagonal Scaling (MDS-BPX) preconditioners for the elast
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Books on the topic "Linear classification methods"

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Ivanov, Yuriy, Roman Filonov, and Dzhaniko Murusidzye. Mechanization and technology of animal husbandry: laboratory practical work. INFRA-M Academic Publishing LLC., 2016. http://dx.doi.org/10.12737/14036.

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In the manual questions of technology, mechanization and automation of processes in animal husbandry are considered; bases of the modern production technology of production of animal husbandry are stated; the short description of designs and the principles of work of the equipment used in animal husbandry is given. All material is issued in the form of the separate laboratory works distributed according to the program of a course. In each laboratory work&#x0D; separate groups of cars and the equipment according to their classification and technological appointment are considered; method of cal
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Nazarov, Vyacheslav, Roman Sandu, and Dmitriy Makarenkov. Technique and technology of combined processing of solid waste. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/996365.

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The educational manual provides information about industrial and domestic waste. The properties of the lithosphere and the soil components. The estimation of soil pollution by industrial and household waste. The peculiarities of classification of wastes and provides criteria for determining risk. Describe the General pattern of the combined methods of processing that use mechanical, physical, thermal and biothermal recycling processes. In detail the construction described granulating equipment, methods of intensification of processes, process flow sheets and engineering calculation methods. Sp
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International Conference on p-Adic Functional Analysis (11th 2010 Université Blaise Pascal). Advances in non-Archimedean analysis: Eleventh International Conference on p-Adic Functional Analysis, July 5-9 2010, Université Blaise Pascal, Clermont-Ferrand, France. Edited by Araujo-Gomez Jesus 1965-, Diarra B. (Bertin) 1944-, and Escassut Alain. American Mathematical Society, 2011.

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Clay Mathematics Institute Workshop on Moduli Spaces of Vector Bundles, with a View toward Coherent Sheaves (2006 Cambridge, Mass.). Grassmannians, moduli spaces, and vector bundles: Clay Mathematics Institute Workshop on Moduli Spaces of Vector Bundles, with a View towards Coherent Sheaves, October 6-11, 2006, Cambridge, Massachusetts. Edited by Ellwood D. (David) 1966- and Previato Emma. American Mathematical Society, 2011.

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Doran, Robert S., Richard V. Kadison, and Efton Park. Operator algebras and their applications: A tribute to Richard V. Kadison : AMS Special Session, Janaury 10-11, 2015, San Antonio, Texas. American Mathematical Society, 2016.

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Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kern
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Ferraty, Frédéric, and Yves Romain, eds. The Oxford Handbook of Functional Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.001.0001.

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This handbook presents the state-of-the-art of the statistics dealing with functional data analysis. With contributions from international experts in the field, it discusses a wide range of the most important statistical topics (classification, inference, factor-based analysis, regression modeling, resampling methods, time series, random processes) while also taking into account practical, methodological, and theoretical aspects of the problems. The book is organised into three sections. Part I deals with regression modeling and covers various statistical methods for functional data such as li
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Min, Renqiang. A non-linear dimensionality reduction method for improving nearest neighbour classification. 2005.

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Min, Renqiang. A non-linear dimensionality reduction method for improving nearest neighbour classification. 2005.

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Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical mod
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Book chapters on the topic "Linear classification methods"

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Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. "Linear Methods for Classification." In The Elements of Statistical Learning. Springer New York, 2008. http://dx.doi.org/10.1007/b94608_4.

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Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. "Linear Methods for Classification." In The Elements of Statistical Learning. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-84858-7_4.

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Hastie, Trevor, Jerome Friedman, and Robert Tibshirani. "Linear Methods for Classification." In The Elements of Statistical Learning. Springer New York, 2001. http://dx.doi.org/10.1007/978-0-387-21606-5_4.

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Hennig, C. "Models and Methods for Clusterwise Linear Regression." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60187-3_17.

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Petrovskiy, Mikhail, and Valentina Glazkova. "Linear Methods for Reduction from Ranking to Multilabel Classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_139.

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Sun, Yi, Mark Robinson, Rod Adams, Paul Kaye, Alistair Rust, and Neil Davey. "Integrating Binding Site Predictions Using Non-linear Classification Methods." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559887_14.

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Wang, Shu-Lin, Hong-Zhu You, Ying-Ke Lei, and Xue-Ling Li. "Performance Comparison of Tumor Classification Based on Linear and Non-linear Dimensionality Reduction Methods." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14922-1_37.

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Joossens, K., and C. Croux. "Empirical Comparison of the Classification Performance of Robust Linear and Quadratic Discriminant Analysis." In Theory and Applications of Recent Robust Methods. Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_12.

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Borgi, Amel, Jean-Michel Bazin, and Herman Akdag. "Two methods of linear correlation search for a knowledge based supervised classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64582-9_802.

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Baione, Fabio, Davide Biancalana, Paolo De Angelis, and Ivan Granito. "Classification Ratemaking via Quantile Regression and a Comparison with Generalized Linear Models." In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89824-7_15.

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Conference papers on the topic "Linear classification methods"

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Zhang, Jian, and Yiming Yang. "Robustness of regularized linear classification methods in text categorization." In the 26th annual international ACM SIGIR conference. ACM Press, 2003. http://dx.doi.org/10.1145/860435.860471.

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Zhou, Wen-Ji, Yang Yu, and Min-Ling Zhang. "Binary Linear Compression for Multi-label Classification." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/496.

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In multi-label classification tasks, labels are commonly related with each other. It has been well recognized that utilizing label relationship is essential to multi-label learning. One way to utilizing label relationship is to map labels to a lower-dimensional space of uncorrelated labels, where the relationship could be encoded in the mapping. Previous linear mapping methods commonly result in regression subproblems in the lower-dimensional label space. In this paper, we disclose that mappings to a low-dimensional multi-label regression problem can be worse than mapping to a classification p
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Guojun Zhou, J. H. L. Hansen, and J. F. Kaiser. "Methods for stress classification: nonlinear TEO and linear speech based features." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.758344.

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Daud, Wan Suhana Wan, Nazihah Ahmad, and Khairu Azlan Abd Aziz. "A review on classification methods for solving fully fuzzy linear systems." In INNOVATION AND ANALYTICS CONFERENCE AND EXHIBITION (IACE 2015): Proceedings of the 2nd Innovation and Analytics Conference & Exhibition. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4937055.

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Bocklitz, Thomas. "Understanding of Non-linear Parametric Regression and Classification Models: A Taylor Series based Approach." In 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007682008740880.

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"COMPARISON OF LINEAR CLASSIFICATION METHODS FOR P300 BRAIN-COMPUTER INTERFACE ON DISABLED SUBJECTS." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003273903280334.

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Liu, Chenghao, Teng Zhang, Peilin Zhao, Jun Zhou, and Jianling Sun. "Locally Linear Factorization Machines." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/319.

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Factorization Machines (FMs) are a widely used method for efficiently using high-order feature interactions in classification and regression tasks. Unfortunately, despite increasing interests in FMs, existing work only considers high order information of the input features which limits their capacities in non-linear problems and fails to capture the underlying structures of more complex data. In this work, we present a novel Locally Linear Factorization Machines (LLFM) which overcomes this limitation by exploring local coding technique. Unlike existing local coding classifiers that involve a p
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Sofwan, Aghus, Imam Santoso, Himawan Pradipta, M. Arfan, and Ajub Ajulian Zahra M. "Normal and Murmur Heart Sound Classification Using Linear Predictive Coding and k-Nearest Neighbor Methods." In 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2019. http://dx.doi.org/10.1109/icicos48119.2019.8982393.

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Shin, Sungtae, Reza Langari, and Reza Tafreshi. "A Performance Comparison of EMG Classification Methods for Hand and Finger Motion." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5993.

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For recognizing human motion intent, electromyogram (EMG) based pattern recognition approaches have been studied for many years. A number of methods for classifying EMG patterns have been introduced in the literature. On the purpose of selecting the best performing method for the practical application, this paper compares EMG pattern recognition methods in terms of motion type, feature extraction, dimension reduction, and classification algorithm. Also, for more usability of this research, hand and finger EMG motion data set which had been published online was used. Time-domain, empirical mode
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Bollegala, Danushka, Kohei Hayashi, and Ken-ichi Kawarabayashi. "Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/552.

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Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings learnt by those methods capture complementary aspects of lexical semantics. Therefore, we believe that it is important to combine the existing word embeddings to produce more accurate and complete meta-embeddings of words. For this purpose, we propose an unsupervised locally linear meta-embedding learning method that takes pre-trained word embeddings as the input, and produces m
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